The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLII-3/W12-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W12-2020, 349–353, 2020
https://doi.org/10.5194/isprs-archives-XLII-3-W12-2020-349-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W12-2020, 349–353, 2020
https://doi.org/10.5194/isprs-archives-XLII-3-W12-2020-349-2020

  06 Nov 2020

06 Nov 2020

AERIAL IMAGE SEGMENTATION IN URBAN ENVIRONMENT FOR VEGETATION MONITORING

J. Martins1, D. A. Sant’Ana2,3, J. Marcato Junior1, H. Pistori3, and W. N. Gonçalves1,4 J. Martins et al.
  • 1Faculty of Engineering, Architecture and Urbanism and Geography, Federal University of Mato Grosso do Sul, Campo Grande, Mato Grosso do Sul, Brazil
  • 2Federal Institute of Mato Grosso do Sul, Aquidauana, Brazil
  • 3Dom Bosco Catholic University, Department of Local Development, Inovisão, Campo Grande, Mato Grosso do Sul, Brazil
  • 4Faculty of Computer Science, Federal University of Mato Grosso do Sul, Campo Grande, Mato Grosso do Sul, Brazil

Keywords: SLIC, Aerial Image, Computer Vision, Classifiers, Geoscience, under-sampling , Machine Learning

Abstract. Urban forests are crucial for the population well-being and improvement of the quality of life. For example, they contribute to the rain damping and to the improvement of the local climate. Therefore a correct and accurate mapping of this resource is fundamental for its correct management. We investigated a method that combines machine learning and SLIC superpixel techniques using different Superpixels (k) number to map trees in the metropolitan region of the municipality of Campo Grande-MS, Brazil with aerial orthoimages with GSD (Ground Sample Distance) of 10 cm. The combination of superpixels and machine learning algorithms were checked out with a set of weka classifiers and achieved good results i.e. F-1 %98.2, MCC %88.4 and Accuracy of %96.8, supporting that this method is efficient when used for urban trees mapping.